class_docstr: Apply a random transformation to the brightness, contrast, saturation and hue of a tensor image.
  
  .. image:: _static/img/ColorJiggle.png
  
  Args:
      p: probability of applying the transformation.
      brightness: The brightness factor to apply.
      contrast: The contrast factor to apply.
      saturation: The saturation factor to apply.
      hue: The hue factor to apply.
      same_on_batch: apply the same transformation across the batch.
      keepdim: whether to keep the output shape the same as input (True) or broadcast it
               to the batch form (False).
  Shape:
      - Input: :math:`(C, H, W)` or :math:`(B, C, H, W)`, Optional: :math:`(B, 3, 3)`
      - Output: :math:`(B, C, H, W)`
  
  .. note::
      This function internally uses :func:`kornia.enhance.adjust_brightness`,
      :func:`kornia.enhance.adjust_contrast`. :func:`kornia.enhance.adjust_saturation`,
      :func:`kornia.enhance.adjust_hue`.
  
  Examples:
      >>> rng = torch.manual_seed(0)
      >>> inputs = torch.ones(1, 3, 3, 3)
      >>> aug = ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.)
      >>> aug(inputs)
      tensor([[[[0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993]],
      <BLANKLINE>
               [[0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993]],
      <BLANKLINE>
               [[0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993],
                [0.9993, 0.9993, 0.9993]]]])
  
  To apply the exact augmenation again, you may take the advantage of the previous parameter state:
      >>> input = torch.randn(1, 3, 32, 32)
      >>> aug = ColorJiggle(0.1, 0.1, 0.1, 0.1, p=1.)
      >>> (aug(input) == aug(input, params=aug._params)).all()
      tensor(True)
code: def apply_transform(
      self,
      input: Tensor,
      params: Dict[str, Tensor],
      flags: Dict[str, Any],
      transform: Optional[Tensor] = None,
  ) -> Tensor:
      transforms = [
          lambda img: adjust_brightness(
              img, params["brightness_factor"] - 1
          ),
          lambda img: adjust_contrast(img, params["contrast_factor"]),
          lambda img: adjust_saturation(
              img, params["saturation_factor"]
          ),
          lambda img: adjust_hue(img, params["hue_factor"] * 2 * pi),
      ]
  
      jittered = input
      for idx in params["order"].tolist():
          t = transforms[idx]
          jittered = t(jittered)
  
      return jittered
  
file_path: kornia/kornia/augmentation/_2d/intensity/color_jiggle.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  from kornia.augmentation import random_generator
  from kornia.augmentation._2d.intensity.base import IntensityAugmentationBase2D
  from kornia.constants import pi
  from kornia.core import Tensor
  from kornia.enhance import adjust_brightness, adjust_contrast, adjust_hue, adjust_saturation
label_desc: Use image node_id 2 for calling the ColorJiggle obj's underlying member method code with example usage: obj.apply_transform(input, params, flags, transform) and returns: jittered